Inter type-2 fuzzy model has been confirmed to be more effective in Takagi–Sugeno (T–S) fuzzy model identification compared to type-1 fuzzy model. It is indisputable that some algorithms based on inter type-2 fuzzy model have already been developed and shown remarkable modeling performance. To further improve the modeling accuracy, the optimization methods and the neural network are taken into consideration. In this paper, an evolving modified inter type-2 fuzzy c-regression model (MIT2-FCRM) algorithm based on gravitational search algorithm (GSA) and a consequent parameter identification method based on extreme learning machine algorithm with forgetting factor for processing online sequences (namely WOS-ELM) were proposed. Then a novel approach for T–S fuzzy modeling was presented, in which, the coefficients of the upper and lower hyperplanes were obtained by evolving MIT2-FCRM algorithm based on GSA, a hyper-plane-shaped membership function (MF) was utilized to identify the antecedent parameters of the T–S fuzzy model, and WOS-ELM was employed to identify the consequent parameters. The modeling results of six examples indicate that the proposed approach is superior to other studies in terms of identification accuracy, compact fuzzy rules and noise resistance ability.

与1型模糊模型相比，Inter 2型模糊模型在Takagi–Sugeno（TS）模糊模型识别中被证实更为有效。毋庸置疑，已经开发出了一些基于内部2型模糊模型的算法，并表现出了出色的建模性能。为了进一步提高建模精度，考虑了优化方法和神经网络。本文提出了一种基于引力搜索算法（GSA）的改进的改进型2型模糊c回归模型（MIT2-FCRM）算法以及基于遗忘因子的基于极端学习机算法的参数识别方法，用于处理在线序列（即WOS-ELM）。然后提出了一种新的T–S模糊建模方法，其中，通过基于GSA的MIT2-FCRM算法进化，获得上下超平面的系数，利用超平面形隶属度函数（MF）识别TS模糊模型的先验参数，并采用WOS-ELM被用来识别结果参数。六个实例的建模结果表明，该方法在识别精度，紧凑的模糊规则和抗噪声能力方面优于其他研究。